Saturday, June 21, 2008

Facial motion capture stabilization is basically where you isolate the movement of the face from the movement of the head. This sounds pretty simple, but it is actually a really difficult problem. In this post I will talk about the general process and give you an example facial stabilization python script.

Disclaimer: The script I have written here is loosely adapted from a MEL script in the book Mocap for Artists, and not something proprietary to Crytek. This is a great book for people of all experience levels, and has a chapter dedicated to facial mocap. Lastly, this script is not padded out or optimized.

To follow this you will need some facial mocap data, there is some freely downloadable here at www.mocap.lt. Grab the FBX file.

Stabilization markers

Get at least 3 markers on the actor that do not move when they move their face. These are called ’stabilization markers’ (STAB markers). You will use these markers to create a coordinate space for the head, so it is important that they not move. STAB markers are commonly found on the left and right temple, and nose bridge. Using a headband and creating virtual markers from multiple solid left/right markers works even better. Headbands move, it’s good to keep this in mind, above you see a special headrig used on Kong to create stable markers.

It is a good idea to write some tools to help you out here. At work I have written tools to parse a performance and tell me the most stable markers at any given time, if you have this data, you can also blend between them.

Load up the facial mocap file you have downloaded, it should look something like this:

In the data we have, you can delete the root, the headband markers, as well as 1-RTMPL, 1-LTMPL, and 1-MNOSE could all be considered STAB markers.

General Pipeline

As you can see, mocap data is just a bunch of translating points. So what we want to do is create a new coordinate system that has the motion of the head, and then use this to isolate the facial movement.

You create a library ‘myLib’ that you load into motionbuilder’s python environment. This is what does the heavy lifting, I say this because you don’t want to do things like send the position of every marker, every frame to your external app via telnet. I also load pyEuclid, a great vector library, because I didn’t feel like writing my own vector class. (MBuilder has no vector class)

Creating ‘myLib’

So we will now create our own library that sits inside MBuilder, this will essentially be a ‘toolkit’ that we communicate with from the outside. Your ‘myLib’ can be called anything, but this should be the place you store functions that do the real processing jobs, you will feed into to them from the outside UI later. The first thing you will need inside the MB python environment is something to cast FBVector3D types into pyEuclid. This is fairly simple:

Next is something that will return an FBModelList of models from an array of names, this is important later when we want to feed in model lists from our external app:

#returns an array of models when given an array of model names#useful with external apps/telnetlib uidef modelsFromStrings(modelNames):
output =[]for name in modelNames:
output.append(FBFindModelByName(name))return output

#returns an array of models when given an array of model names
#useful with external apps/telnetlib ui
def modelsFromStrings(modelNames):
output = []
for name in modelNames:
output.append(FBFindModelByName(name))
return output

Now, if you were to take these snippets and save them as a file called myLib.py in your MBuilder directory tree (MotionBuilder75 Ext2\bin\x64\python\lib), you can load them into the MBuilder environment. (You should have also placed pyEuclid here)

It’s always good to mock-up code in telnet because, unlike the python console in MBuilder, it supports copy/paste etc..

In the image above, I get the position of a model in MBuilder, it returns as a FBVector3D, I then import myLib and pyEuclid and use our function above to ‘cast’ the FBVector3d to a pyEuclid vector. It can now be added, subtracted, multiplied, and more; all things that are not possible with the default MBuilder python tools. Our other function ‘fbv()‘ casts pyEuclid vectors back to FBVector3d, so that MBuilder can read them.

So we can now do vector math in motionbuilder! Next we will add some code to our ‘myLib’ that stabilizes the face.

Adding Stabilization-Specific Code to ‘myLib’

One thing we will need to do a lot is generate ‘virtual markers’ from the existing markers. To do this, we need a function that returns the average position of however many vectors (marker positions) it is fed.

We feed our ‘stab‘function FBModelLists of right, left, and center stabilization markers, it creates virtual markers from these groups. Then ‘markers’ is all the markers to be stabilized. ‘leavrOrig’ is an option I usually add, this allows for non-destructive use, I have just made the fn leave original in this example, as I favor this, so this option does nothing, but you could add it. With the original markers left, you can immediately see if there was an error in your script. (new motion should match orig)

Creating an External UI that Uses ‘myLib’

Earlier I mentioned Creating Interactive MotionBuilder User Interface Tools, where I explain how to screenscrape/use the telnet Python Remote Server to create an interactive external UI that floats as a window in MotionBuilder itself. I also use the libraries mentioned in the above article.

The code for the facial stabilization UI I have created is here: [stab_ui.py]

I will now step through code snippets pertaining to our facial STAB tool:

This returns a list of strings that are the currently selected models in MBuilder. This is the main thing that our external UI does. The person needs to interactively choose the right, left, and center markers, then all the markers that will be stabilized.

At the left here you see what the UI looks like. To add some feedback to the buttons, you can make them change to reflect that the user has selected markers. We do so by changing the button text.

This also stores all the markers the user has chosen into the variable ‘rStabMarkers‘. Once we have all the markers the user has chosen, we need to send them to ‘myLib‘ in MBuilder so that it can run our ‘stab‘ function on them. This will happen when they click ‘Stabilize Markerset‘.

Above we now use ‘modelsFromStrings‘ to feed ‘myLib’ the names of selected models. When you run this on thousands of frames, it will actually hang for up to a minute or two while it does all the processing. I discuss optimizations below. Here is a video of what you should have when stabilization is complete:

Kill the keyframes on the root (faceAttach) to remove head motion

Conclusion: Debugging/Optimization

Remember: Your stabilization will only be as good as your STAB markers. It really pays off to create tools to check marker stability.

Sometimes the terminal/screen scraping runs into issues. The mbPipe function can be padded out a lot and made more robust, this here was just an example. If you look at the external python console, you can see exactly what mbPipe is sending to MBuilder, and what it is receiving back through the terminal:

All of the above can be padded out and optimized. For instance, you could try to do everything without a single lPlayerControl.StepForward() or lScene.Evaluate(), but this takes a lot of MotionBuilder/programming knowhow; it involves only using the keyframe data to generate your matrices, positions etc, and never querying a model.